d14150da75
Implement intelligent agent learning from Knowledge Graph execution history with per-task-type expertise tracking, recency bias, and learning curves. ## Phase 5.3 Implementation ### Learning Infrastructure (✅ Complete) - LearningProfileService with per-task-type expertise metrics - TaskTypeExpertise model tracking success_rate, confidence, learning curves - Recency bias weighting: recent 7 days weighted 3x higher (exponential decay) - Confidence scoring prevents overfitting: min(1.0, executions / 20) - Learning curves computed from daily execution windows ### Agent Scoring Service (✅ Complete) - Unified AgentScore combining SwarmCoordinator + learning profiles - Scoring formula: 0.3*base + 0.5*expertise + 0.2*confidence - Rank agents by combined score for intelligent assignment - Support for recency-biased scoring (recent_success_rate) - Methods: rank_agents, select_best, rank_agents_with_recency ### KG Integration (✅ Complete) - KGPersistence::get_executions_for_task_type() - query by agent + task type - KGPersistence::get_agent_executions() - all executions for agent - Coordinator::load_learning_profile_from_kg() - core KG→Learning integration - Coordinator::load_all_learning_profiles() - batch load for multiple agents - Convert PersistedExecution → ExecutionData for learning calculations ### Agent Assignment Integration (✅ Complete) - AgentCoordinator uses learning profiles for task assignment - extract_task_type() infers task type from title/description - assign_task() scores candidates using AgentScoringService - Fallback to load-based selection if no learning data available - Learning profiles stored in coordinator.learning_profiles RwLock ### Profile Adapter Enhancements (✅ Complete) - create_learning_profile() - initialize empty profiles - add_task_type_expertise() - set task-type expertise - update_profile_with_learning() - update swarm profiles from learning ## Files Modified ### vapora-knowledge-graph/src/persistence.rs (+30 lines) - get_executions_for_task_type(agent_id, task_type, limit) - get_agent_executions(agent_id, limit) ### vapora-agents/src/coordinator.rs (+100 lines) - load_learning_profile_from_kg() - core KG integration method - load_all_learning_profiles() - batch loading for agents - assign_task() already uses learning-based scoring via AgentScoringService ### Existing Complete Implementation - vapora-knowledge-graph/src/learning.rs - calculation functions - vapora-agents/src/learning_profile.rs - data structures and expertise - vapora-agents/src/scoring.rs - unified scoring service - vapora-agents/src/profile_adapter.rs - adapter methods ## Tests Passing - learning_profile: 7 tests ✅ - scoring: 5 tests ✅ - profile_adapter: 6 tests ✅ - coordinator: learning-specific tests ✅ ## Data Flow 1. Task arrives → AgentCoordinator::assign_task() 2. Extract task_type from description 3. Query KG for task-type executions (load_learning_profile_from_kg) 4. Calculate expertise with recency bias 5. Score candidates (SwarmCoordinator + learning) 6. Assign to top-scored agent 7. Execution result → KG → Update learning profiles ## Key Design Decisions ✅ Recency bias: 7-day half-life with 3x weight for recent performance ✅ Confidence scoring: min(1.0, total_executions / 20) prevents overfitting ✅ Hierarchical scoring: 30% base load, 50% expertise, 20% confidence ✅ KG query limit: 100 recent executions per task-type for performance ✅ Async loading: load_learning_profile_from_kg supports concurrent loads ## Next: Phase 5.4 - Cost Optimization Ready to implement budget enforcement and cost-aware provider selection.
212 lines
4.1 KiB
Markdown
212 lines
4.1 KiB
Markdown
# VAPORA v1.0 - Quick Start Deployment
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**5-Minute Production Deployment Guide**
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---
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## Prerequisites Check
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```bash
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# Verify you have these tools
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kubectl version --client # Kubernetes CLI
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docker --version # Docker for building images
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nu --version # Nushell for scripts
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```
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---
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## Step 1: Build Docker Images (5 minutes)
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```bash
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# From project root
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# Build all images and push to Docker Hub
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nu scripts/build-docker.nu --registry docker.io --tag v0.1.0 --push
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# Or build locally (no push)
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nu scripts/build-docker.nu
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```
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**Output**: 4 Docker images built (~175MB total)
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---
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## Step 2: Configure Secrets (2 minutes)
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```bash
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# Edit secrets file
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nano kubernetes/03-secrets.yaml
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# Replace these values:
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# - jwt-secret: $(openssl rand -base64 32)
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# - anthropic-api-key: sk-ant-xxxxx
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# - openai-api-key: sk-xxxxx
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# - surrealdb-pass: $(openssl rand -base64 32)
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```
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**NEVER commit this file with real secrets!**
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---
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## Step 3: Configure Ingress (1 minute)
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```bash
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# Edit ingress file
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nano kubernetes/08-ingress.yaml
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# Update this line:
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# - host: vapora.yourdomain.com # Change to your domain
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```
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---
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## Step 4: Deploy to Kubernetes (3 minutes)
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```bash
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# Dry run to validate
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nu scripts/deploy-k8s.nu --dry-run
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# Deploy for real
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nu scripts/deploy-k8s.nu
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# Wait for all pods to be ready
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kubectl wait --for=condition=ready pod -l app -n vapora --timeout=300s
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```
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**Output**: 11 pods running (2 backend, 2 frontend, 3 agents, 1 mcp, 1 db, 1 nats)
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---
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## Step 5: Verify Deployment (2 minutes)
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```bash
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# Check all pods are running
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kubectl get pods -n vapora
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# Check services
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kubectl get svc -n vapora
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# Get ingress IP/hostname
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kubectl get ingress -n vapora
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# Test health endpoints
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kubectl exec -n vapora deploy/vapora-backend -- curl -s http://localhost:8080/health
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```
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---
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## Step 6: Access VAPORA
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1. **Configure DNS**: Point your domain to ingress IP
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2. **Access UI**: `https://vapora.yourdomain.com`
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3. **Check health**: `https://vapora.yourdomain.com/api/v1/health`
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---
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## Troubleshooting
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### Pods not starting?
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```bash
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kubectl describe pod -n vapora <pod-name>
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kubectl logs -n vapora <pod-name>
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```
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### Can't connect to database?
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```bash
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kubectl logs -n vapora surrealdb-0
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kubectl exec -n vapora deploy/vapora-backend -- curl http://surrealdb:8000/health
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```
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### Image pull errors?
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```bash
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# Check if images exist
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docker images | grep vapora
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# Create registry secret
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kubectl create secret docker-registry regcred \
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-n vapora \
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--docker-server=docker.io \
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--docker-username=<user> \
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--docker-password=<pass>
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```
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---
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## Alternative: Provisioning Deployment
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For advanced deployment with service mesh and auto-scaling:
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```bash
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cd provisioning/vapora-wrksp
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# Validate configuration
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nu scripts/validate-provisioning.nu
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# Deploy full stack
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provisioning workflow run workflows/deploy-full-stack.yaml
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```
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See: [`provisioning-integration/README.md`](provisioning-integration/README.md)
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---
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## Next Steps
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- [ ] Set up monitoring (Prometheus + Grafana)
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- [ ] Configure TLS certificates (cert-manager)
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- [ ] Set up backups for SurrealDB
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- [ ] Configure HPA (Horizontal Pod Autoscaler)
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- [ ] Enable log aggregation
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- [ ] Test agent workflows
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---
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## Full Documentation
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- **Comprehensive Guide**: [`DEPLOYMENT.md`](DEPLOYMENT.md)
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- **K8s README**: [`kubernetes/README.md`](kubernetes/README.md)
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- **Provisioning Guide**: [`provisioning-integration/README.md`](provisioning-integration/README.md)
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- **Project Overview**: [`PROJECT_COMPLETION_REPORT.md`](PROJECT_COMPLETION_REPORT.md)
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---
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## Quick Commands Reference
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```bash
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# Build images
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nu scripts/build-docker.nu --push
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# Deploy
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nu scripts/deploy-k8s.nu
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# Validate
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nu scripts/validate-deployment.nu
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# Validate Provisioning
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nu scripts/validate-provisioning.nu
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# Check status
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kubectl get all -n vapora
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# View logs
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kubectl logs -n vapora -l app=vapora-backend -f
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# Scale agents
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kubectl scale deployment vapora-agents -n vapora --replicas=5
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# Rollback
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kubectl rollout undo deployment/vapora-backend -n vapora
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# Uninstall
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kubectl delete namespace vapora
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```
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---
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**VAPORA v1.0** - Production Ready ✅
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**Total Deployment Time**: ~15 minutes
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**Status**: All 5 phases completed
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